In the global economy, domestic manufacturing enterprises are facing formidable competition from foreign companies who offer high quality goods at low prices. Domestic enterprises cannot remain competitive with foreign or even domestic companies by manufacturing goods in accordance with conventional practices. The intense present day competition necessitates rapid and, indeed, continual improvement of the methods and facilities for manufacturing goods.
There is a bewildering array of technological innovations available to enable a company to improve its manufacturing processes. An enterprise may choose to automate its factory with robots, MPR and/or computer integrated manufacturing. It may often reduce direct labor costs and improve employee performance through motivational techniques. Improvements involving automation, however, are likely to be very expensive and to demonstrably affect the continued viability of the enterprise in a competitive market. Thereafter, accurate analysis of manufacturing processes and investment in improving existing processes is critical to performance. Difficult questions have to be asked and answered about what is to be improved, how to improve it, and in what order to make the improvements for the optimal cost-efficient performance.
Unfortunately, decision makers often rely more on intuition than on an accurate analysis. Reliance on intuition, more often than not, proves to be misleading. Intuition is often misguided by outdated beliefs or misunderstandings of the principles of manufacturing. Very often intuition does not lead to lower costs or higher quality. Indeed, these older manufacturing Principles may have the reverse effect. To cite one example, most American manufacturers use a batch production method in which batch sizes for a manufacturing process are increased so as to reduce the direct manufacturing cost per unit. Contrary to intuition, however, running larger batch sizes can actually increase indirect manufacturing costs and conceal waste functions that are likely to impede efforts to lower manufacturing costs and improve the product quality. Waste functions may include, for example, excessively long set-up times for each manufacturing process, the amount of scrap produced by a process, the amount of rework that is done, the effect of machine and human down time. These waste functions necessitate, for a given volume of material, more labor, more inventory, more capital equipment, more time, and more physical space. Thus, overhead, plant and capital costs are increased with batch manufacturing. Furthermore, increased batch sizes inevitably affect quality negatively. Finally, running large batch sizes makes it more costly to build custom products that many markets demand, decreases responsiveness to changing market conditions, and slows the introduction of new products.
The executive tasked with improving the manufacturing processes of an enterprise must therefore ignore intuition and seek guidance for improving the manufacturing process with sound manufacturing principles.
One approach that overcomes misguided intuition is the "Just In Time" or "Toyota" method. The basic tenant of "Just In Time" is that an existing shipment or factory output schedule should be met with ever smaller batch sizes of the raw materials and intermediate products that make up the final product. With "Just In Time", batch sizes are made increasingly smaller until a particular workstation fails. Appropriate adjustments are then made in the manufacturing process. The "Just In Time" method replaces misguided intuition by basing improvements on reduction of batch sizes.
Although superior to intuition, "Just In Time" has its drawbacks. Improvements to a factory using the "Just In Time" method are made slowly and can result in temporary but sometimes lengthy halts in production. Since batch size reductions are necessary to gather information on which processes in the factory require the most improvement, the method is only suitable for the manufacture of products in large lots, such as automobiles. Many enterprises lack the time, money or volume of production to make them suitable candidates for improvement by the "Just In Time" method.
A tool with which to analyze a manufacturing process before making the changes to the processes is therefore required for those not able to use "Just In Time". Indeed, even those using "Just In Time" will benefit from this sort of tool.
One type of prior art tool is one that dynamically models the real-time operation of a factory. Modeling languages such as SLAM and GPSS have been successfully used to model manufacturing processes. Successful use of these languages, however, requires expert computer programming skills. Normally, those tasked with improving the manufacturing process are manufacturing executives and engineers, not expert programmers, and lack the skills necessary to apply the modeling language techniques to their particular processes.
Moreover, dynamic simulation tools suffer from other significant shortcomings. The accuracy of the program's simulation is limited by the modeler's insight and understanding of the manufacturing process. These real-time models merely simulate the movement of material through the various manufacturing processes by monitoring the size of the queues of material at various points in the factory. Apart from showing that a process in the factory has either too much material or not enough material to process, the size of the queues of material waiting to be processed do not provide information useful in determining what component of the manufacturing process should be improved or how to improve it. Multiple hypothetical runs must be made to see what effect a given set of parameter changes will have on performance. Information about what changes in the process will yield the most significant improvement therefore must be discovered by trial and error. With a very large factory, in which multiple processes are running simultaneously, the use of such programs to simulate real-time production is so difficult that it is almost impossible to predict the effects of changes in a manufacturing processes.
To improve the efficiency of manufacturing processes in a factory, an analytical tool simple and easy enough to be used by non-expert programmers is needed for accurately modelling the manufacturing process, identifying the steps or processes which are candidates for improvement, prioritizing the candidates for improvement, and determining the character and quantity of improvement.